Kaggle은 뉴욕시에서 택시 여행의 총 주행 거리를 예측하는 모델을 만드는 것에 도전하고 있습니다.
기본 데이터 세트는 픽업 시간, 지리적 좌표, 승객 수 및 기타 여러 변수가 포함 된 NYC 택시 및 리무진위원회에서 발급 한 데이터 세트입니다.
우리가 2015 년에 주최 한 ECML / PKDD 여행 시간 도전과 유사하다는 것을 인정할 것입니다. 그러나 이 도전은 뒤죽박죽입니다.
우리는 다른 참가자가 자신의 예측에 사용할 수있는 추가 교육 데이터를 게시하도록 (현금 상금과 함께) 당신을 격려합니다.
우리는 커뮤니티에 특히 통찰력 있거나 가치있는 커널 작성자에게 보상하기 위해 격주 및 최종 상을 지정했습니다.
id,trip_duration
id00001,978
id00002,978
id00003,978
id00004,978
etc.
경쟁 데이터 세트는 Google Cloud Platform의 Big Query에서 제공되는 2016 년 NYC Yellow Cab 여행 기록 데이터를 기반으로합니다.
이 데이터는 원래 NYC 택시 및 리무진위원회 (TLC)에서 발간 한 것입니다.
참가자는 개별 여행 속성에 따라 테스트 세트의 각 여행 기간을 예측해야 합니다.
Y = 저장 및 전달; N = 상점 및 순회 여행 불가
trip_duration : 여행 기간 (초)
면책 조항 : 커널에서 사용할 확장 된 변수 집합을 제공하기 위해 데이터 집합 순서에서 드롭 오프 좌표를 제거하지 않기로 결정했습니다.
#install.packages(c('flexdashboard', 'TraMineR', 'leaflet', 'treemap', 'highcharter', 'zoo')
#라이브러리 로딩
library(data.table)
library(dplyr)
library(ggplot2)
library(flexdashboard)
library(TraMineR)
library(highcharter)
library(DT)
library(flexdashboard)
library(leaflet)
library(rmarkdown)
library(treemap)
library(viridisLite)
library(tidyverse)
library(geosphere)
library(caret)
library(ggmap)
library(scales)
library(ggthemes)
library(gridExtra)
library(sp)
library(lubridate)
library(grid)
rm(list=ls())
fillColor = "#ff9999"
train = read_csv("./data/train.csv")
Parsed with column specification:
cols(
id = col_character(),
vendor_id = col_integer(),
pickup_datetime = col_datetime(format = ""),
dropoff_datetime = col_datetime(format = ""),
passenger_count = col_integer(),
pickup_longitude = col_double(),
pickup_latitude = col_double(),
dropoff_longitude = col_double(),
dropoff_latitude = col_double(),
store_and_fwd_flag = col_character(),
trip_duration = col_integer()
)
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test = read_csv("./data/test.csv")
Parsed with column specification:
cols(
id = col_character(),
vendor_id = col_integer(),
pickup_datetime = col_datetime(format = ""),
passenger_count = col_integer(),
pickup_longitude = col_double(),
pickup_latitude = col_double(),
dropoff_longitude = col_double(),
dropoff_latitude = col_double(),
store_and_fwd_flag = col_character()
)
sum(is.na(train))
[1] 0
sum(is.na(test))
[1] 0
ggplot(data=train, aes(x= trip_duration)) +
geom_histogram(bins = 100) +
scale_x_log10(limits = c(NA,100000)) +
scale_y_log10() +
theme_bw() +
theme(axis.title = element_text(size=16),
axis.text = element_text(size=14)) +
labs(x = 'Trip Duration', y = 'Count', title = 'Trip Duration')
nycData = subset(train,train$trip_duration < (60*60*24) )
ggplot(nycData,aes(x=factor(passenger_count),y=trip_duration))+geom_boxplot()+scale_y_log10()
ggplot(data=nycData, aes(x= pickup_longitude)) +
geom_histogram(bins = 100) +
scale_x_continuous(limits = c(-74,-73.85)) +
theme_bw() +
theme(axis.title = element_text(size=16),
axis.text = element_text(size=14)) +
labs(x = 'Longitude', y = 'Count', title = 'Longitude')
Warning message:
In strsplit(content, "\n", fixed = TRUE) : input string 1 is invalid UTF-8
ggplot(data=nycData, aes(x= pickup_latitude)) +
geom_histogram(bins = 100) +
scale_x_continuous(limits = c(40.6,40.85)) +
theme_bw() +
theme(axis.title = element_text(size=16),
axis.text = element_text(size=14)) +
labs(x = 'Latitude', y = 'Count', title = 'Latitude')
pick_coord <- nycData %>%
select(pickup_longitude, pickup_latitude)
drop_coord <- nycData %>%
select(dropoff_longitude, dropoff_latitude)
nycData$dist <- distCosine(pick_coord, drop_coord)
nycData$haversine <- distHaversine(pick_coord, drop_coord)
nycData$bearing <- bearing(pick_coord, drop_coord)
ggplot(data=nycData, aes(x= haversine)) +
geom_histogram() +
scale_x_log10() +
scale_y_log10() +
theme_bw() +
theme(axis.title = element_text(size=16),
axis.text = element_text(size=14)) +
labs(x = 'Distance', y = 'Count', title = 'Distance')
ggplot(nycData)+
geom_point(aes(x=haversine,y=trip_duration))+
scale_y_log10() +
scale_x_log10() +
theme_bw()+
theme(axis.title = element_text(size=16),axis.text = element_text(size=14))+
xlab("(Distance)")+
ylab("Duration")
train <- as.tibble(train)
test <- as.tibble(test)
combine = bind_rows(train %>% mutate(dset="train"),
test %>% mutate(dset="test",
dropoff_datetime=NA,
trip_duration=NA))
combine <- combine %>% mutate(dset = factor(dset))
train = train %>%
mutate(pickup_datetime = ymd_hms(pickup_datetime),
dropoff_datetime = ymd_hms(dropoff_datetime),
vendor_id = factor(vendor_id),
passenger_count = factor(passenger_count))
각 변수의 분포를 우선 살펴본다
pickup/dropoff coordinates
set.seed(1234)
foo <- sample_n(train, 8e3)
leaflet(data = foo) %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
addCircleMarkers(~ pickup_longitude, ~pickup_latitude, radius = 1,
color = "blue", fillOpacity = 0.3)
leaflet(data = foo) %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
addCircleMarkers(~ dropoff_longitude, ~dropoff_latitude, radius = 1,
color = "blue", fillOpacity = 0.3)
trip_duration
train %>%
ggplot(aes(trip_duration)) +
geom_histogram(fill = "red", bins = 150) +
scale_x_log10() +
scale_y_sqrt()
# View(train)
p1 <- train %>%
ggplot(aes(pickup_datetime)) +
geom_histogram(fill = "red", bins = 120) +
labs(x = "Pickup dates")
p2 <- train %>%
ggplot(aes(dropoff_datetime)) +
geom_histogram(fill = "blue", bins = 120) +
labs(x = "Dropoff dates")
layout <- matrix(c(1,2),2,1,byrow=FALSE)
multiplot(p1, p2, layout=layout)
p1 <- train %>%
group_by(passenger_count) %>%
count() %>%
ggplot(aes(passenger_count, n, fill = passenger_count)) +
geom_col() +
scale_y_sqrt() +
theme(legend.position = "none")
p2 <- train %>%
ggplot(aes(vendor_id, fill = vendor_id)) +
geom_bar() +
theme(legend.position = "none")
p3 <- train %>%
ggplot(aes(store_and_fwd_flag)) +
geom_bar() +
theme(legend.position = "none") +
scale_y_log10()
p4 <- train %>%
mutate(wday = wday(pickup_datetime, label = TRUE)) %>%
group_by(wday, vendor_id) %>%
count() %>%
ggplot(aes(wday, n, colour = vendor_id)) +
geom_point(size = 4) +
labs(x = "Day of the week", y = "Total number of pickups") +
theme(legend.position = "none")
p5 <- train %>%
mutate(hpick = hour(pickup_datetime)) %>%
group_by(hpick, vendor_id) %>%
count() %>%
ggplot(aes(hpick, n, color = vendor_id)) +
geom_point(size = 4) +
labs(x = "Hour of the day", y = "Total number of pickups") +
theme(legend.position = "none")
layout <- matrix(c(1,2,3,4,5,5),3,2,byrow=TRUE)
multiplot(p1, p2, p3, p4, p5, layout=layout)
p1 <- train %>%
mutate(hpick = hour(pickup_datetime),
Month = factor(month(pickup_datetime, label = TRUE))) %>%
group_by(hpick, Month) %>%
count() %>%
ggplot(aes(hpick, n, color = Month)) +
geom_line(size = 1.5) +
labs(x = "Hour of the day", y = "count")
p2 <- train %>%
mutate(hpick = hour(pickup_datetime),
wday = factor(wday(pickup_datetime, label = TRUE))) %>%
group_by(hpick, wday) %>%
count() %>%
ggplot(aes(hpick, n, color = wday)) +
geom_line(size = 1.5) +
labs(x = "Hour of the day", y = "count")
layout <- matrix(c(1,2),2,1,byrow=FALSE)
multiplot(p1, p2, layout=layout)
Pickup date/time vs trip_duration
p1 <- train %>%
mutate(wday = wday(pickup_datetime, label = TRUE)) %>%
group_by(wday, vendor_id) %>%
summarise(median_duration = median(trip_duration)/60) %>%
ggplot(aes(wday, median_duration, color = vendor_id)) +
geom_point(size = 4) +
labs(x = "Day of the week", y = "Median trip duration [min]")
p2 <- train %>%
mutate(hpick = hour(pickup_datetime)) %>%
group_by(hpick, vendor_id) %>%
summarise(median_duration = median(trip_duration)/60) %>%
ggplot(aes(hpick, median_duration, color = vendor_id)) +
geom_smooth(method = "loess", span = 1/2) +
geom_point(size = 4) +
labs(x = "Hour of the day", y = "Median trip duration [min]") +
theme(legend.position = "none")
layout <- matrix(c(1,2),2,1,byrow=FALSE)
multiplot(p1, p2, layout=layout)
train %>%
ggplot(aes(trip_duration, fill = vendor_id)) +
geom_density(position = "stack") +
scale_x_log10()
train %>%
group_by(vendor_id) %>%
summarise(mean_duration = mean(trip_duration),
median_duration = median(trip_duration))
픽업 포인트와 드롭 오프 포인트의 좌표로부터 두 점 사이의 거리를 계산할 수 있습니다.
이 거리를 계산하기 위해 우리는 지구권 패키지의 distHaversine 함수를 사용하고 있습니다. 이 방법은 구형 지구의 두 점 사이의 최단 거리를 제공합니다.
train = as.data.table(train)
train <- train[,distance_km :=
distHaversine(matrix(c(pickup_longitude, pickup_latitude), ncol = 2),
matrix(c(dropoff_longitude,dropoff_latitude), ncol = 2))/1000
]
train %>%
ggplot(aes(x=distance_km)) +
geom_histogram(bins=4000, fill="red")+
theme_bw()+theme(axis.title = element_text(size=11),axis.text = element_text(size=8))+
ylab("Density")+coord_cartesian(x=c(0,25))
train[,speed:=(distance_km)/(trip_duration/3600)]
train %>%
ggplot(aes(x=speed)) +
geom_histogram(bins=4000, fill="red")+
theme_bw()+theme(axis.title = element_text(size=11),axis.text = element_text(size=8))+
ylab("Density")+coord_cartesian(x=c(0,50))
summary(train$speed)
Min. 1st Qu. Median
0.000 9.131 12.810
Mean 3rd Qu. Max.
14.440 17.860 9285.000
train$pickup_hour <- hour(train$pickup_datetime)
train$pickup_week <- week(train$pickup_datetime)
train$pickup_month <- month(train$pickup_datetime)
train$pickup_weekdays <- weekdays(train$pickup_datetime)
train$pickup_weekend <- ifelse(train$pickup_weekdays==1 | train$pickup_weekdays==7,"Weekend","not-Weekend")
train[,pickup_datetime:=as.Date(pickup_datetime)]
train[,dropoff_datetime:=as.Date(dropoff_datetime)]
train[,":="(
pickup_yday=yday(pickup_datetime)
,pickup_mday=mday(pickup_datetime)
)]
train %>%
group_by(pickup_hour) %>%
summarize(mean_speed = mean(speed),n()) %>%
ggplot(aes(x=pickup_hour,y=mean_speed))+
geom_smooth(method = 'loess',color="grey10")+
geom_point(color="red")+coord_cartesian(ylim=c(10,25))+theme_bw()
library(corrplot)
corr_features = train[,.(pickup_hour, pickup_week, pickup_month,pickup_yday, pickup_mday,passenger_count,trip_duration,distance_km)]
corrplot(cor(corr_features, use='complete.obs'), type='lower')
변수의 어느 것도 trip_duration과 상관 관계가 없기 때문에 이것은 매우 불안정한 구성입니다.
거리가 상관 관계가있는 유일한 거리이지만 테스트 세트에는 해당 기능이 없습니다. 기능의 일부가 대상 변수와 상호 연관 될 수도 있지만 조사해야합니다.
피쳐와 타겟 변수간에 상관 관계가 없다는 것은 트립 시간을 예측하기 위해 외부 피쳐를 찾아야한다는 것을 의미합니다.
plot1 <-train[, list(mean_trip_duration = mean(trip_duration)), by = pickup_weekdays] %>%
ggplot(aes(x = pickup_weekdays, y = mean_trip_duration)) +
geom_bar(stat = 'identity', fill = 'steelblue') +
labs(x = 'Month', y = 'Mean Trip Duration', title = 'Mean Trip duration by weekdays')
grid.arrange(plot1)
plot1 <-train[, list(mean_trip_duration = mean(trip_duration)), by = pickup_hour] %>%
ggplot(aes(x = as.factor(pickup_hour), y = mean_trip_duration)) +
geom_bar(stat = 'identity', fill = 'steelblue') +
labs(x = 'Hours', y = 'Mean Trip Duration', title = 'Mean Trip duration by hour of the day')
plot2 = train[,.N, by=pickup_hour] %>%
ggplot(aes(x=pickup_hour, y=N)) +
geom_bar(stat='identity', fill='steelblue') +
labs(x='', y='Number of Rides', title='Total Rides Per Hour')
grid.arrange(plot1, plot2, ncol =2)
Open Source Routing Machine, OSRM을 사용하여 oscarleo가 유용한 데이터 세트를 제공합니다.
픽업에서 드롭 오프 위치까지의 가장 빠른 경로와 해당 시간. 가장 빠른 노선을위한 거리의 수.
예를 들어 좌회전이나 우회전과 같은 여행 당 길 찾기.
suppressMessages({
fastest_route_train = read_csv("./data/new-york-city-taxi-with-osrm/fastest_routes_train_part_1.csv")
})
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dtrain = merge(train, fastest_route_train, by="id")
dtrain[,number_of_streets := number_of_steps - 1]
plot1 <-
dtrain[, list(mean_trip_duration = mean(total_travel_time)), by = number_of_streets] %>%
ggplot(aes(x = as.factor(number_of_streets), y = mean_trip_duration)) +
geom_bar(stat = 'identity', fill = 'steelblue') +
labs(x = 'Number of Streets', y = 'Mean Trip Duration', title = 'Mean Trip duration by Number of Streets')
plot2 <- dtrain[, list(Number_of_Rides = .N), by = number_of_streets] %>%
ggplot(aes(x = as.factor(number_of_streets), y = Number_of_Rides)) +
geom_bar(stat = 'identity', fill = 'steelblue') +
labs(x = 'Number of Streets', y = 'Number of Trips', title = 'Number of Rides by Number of Streets')
plot3 <- dtrain[, list(mean_distance = mean(total_distance)/1000), by = number_of_streets] %>%
ggplot(aes(x = as.factor(number_of_streets), y = mean_distance)) +
geom_bar(stat = 'identity', fill = 'steelblue') +
labs(x = 'Number of Streets', y = 'Mean Trip Distnace(km)', title = 'Mean Trip Distance by Number of Streets')
grid.arrange(plot1,plot2, plot3)
plot1 <- dtrain %>%
ggplot(aes(trip_duration)) +
geom_density(fill = "red", alpha = 0.5) +
geom_density(aes(total_travel_time), fill = "blue", alpha = 0.5) +
scale_x_log10() +
coord_cartesian(xlim = c(5e1, 8e3))
dtrain[,diff:= abs(trip_duration-total_travel_time)]
dtrain[,number_of_steps:= ifelse(number_of_steps>= 25, 25, number_of_steps)]
plot2 = dtrain[, list(mean_distance_km = mean(diff)), by=number_of_steps-1] %>%
ggplot(aes(x=number_of_steps, y=mean_distance_km)) +
geom_bar(stat='identity', fill='steelblue') +
labs(x='Number of Streets', y='|TripDuration - TotalTravelTime)|')
grid.arrange(plot1, plot2, ncol=2)
파란색 그림은 가장 빠른 경로 밀도 그림입니다. 실제 여행 시간과 비슷하지만 교대로 보입니다.
두 번째 플롯에서 우리는 실제 이동량과 가장 빠른 경로 시간 간의 절대적인 차이가 거리의 수가 증가함에 따라 증가한다는 것을 관찰합니다.
오늘날의 가장 빠른 경로가 과거의 실제 경로와 다른 확률이 단계 증가의 수로 증가하기 때문에 논리적입니다.
가장 빠른 경로 데이터를 사용하여 실제 경로에 대해 의미있는 것을 말하는 방법을 이해하는 것은 정말 어렵습니다. 오늘 가장 빠른 길은 내일 또는 과거의 가장 빠른 길이 아니기 때문에.
따라서 훈련 세트의 경로는 OSRM에서 제안한 경로와 다릅니다. 내가 가장 다른 점은 가장 빠른 경로와 관련된 기능이 훈련 세트의 rides에 대해별로 말하지 않는다는 것입니다.
이 데이터 세트는 Debanjan이 Google Maps API를 사용하여 제공합니다.
적어도 이 대회의 모든 라이딩에 사용할 수있는 데이터가 있다면 많은 것을 약속합니다.
데이터는 훈련 세트의 하위 집합에 대해서만 제공되므로 나머지 데이터는 잠깐 기다리고 있습니다. 이 데이터 세트의 중요한 정보는 Google 기간이라는 두 위치 간의 과거 평균 지속 시간입니다.
나는 Google 기간과 실제 기간의 차이로 새로운 기능을 만들어 낼 것입니다. 차이가 ’지연’보다 ’0’보다 작으면 ’초기 도착’이 있습니다.
suppressMessages({
google_dist = read_csv("./data/new-york-city-taxi-with-osrm/train_distance_matrix.csv")
})
google_dist = data.table(google_dist)
google_dist[, diff := google_duration-trip_duration]
plot1 <- google_dist %>%
ggplot(aes(trip_duration)) +
geom_density(fill = "red", alpha = 0.5) +
geom_density(aes(google_duration), fill = "blue", alpha = 0.5) +
scale_x_log10() +
coord_cartesian(xlim = c(5e1, 8e3))
plot2 = google_dist %>%
ggplot(aes(x=diff)) +
geom_histogram(bins=20000, fill="red")+
theme_bw()+theme(axis.title = element_text(size=12),axis.text = element_text(size=12))+
ylab("Density")+coord_cartesian(x=c(-2000,2000))
grid.arrange(plot1, plot2, ncol=2)
파란색 줄거리는 Google 기간이며 빨간색은 실제 지속 시간입니다.
Google 기간이 더 비뚤어졌습니다. 실제 지속 시간은 더 높은 분산 (더 부풀어 오른)과 더 두꺼운 꼬리를가집니다.
두 번째 플롯은 Google 기간과 실제 기간 간의 시간 차이에 대한 막대 그래프입니다.
왼쪽보다 0의 오른쪽에 더 많은 관측이 있다는 것을 관찰 할 수 있습니다. 즉, ’지연’보다 ’조기 도착’이 더 많은 ’rides’가 있습니다.
weather = fread("./data/new-york-city-taxi-with-osrm/weather_data_nyc_centralpark_2016.csv")
weather <- weather %>%
mutate(date = dmy(date),
rain = as.numeric(ifelse(precipitation == "T", "0.01", precipitation)),
s_fall = as.numeric(ifelse(`snow fall` == "T", "0.01", `snow fall`)),
s_depth = as.numeric(ifelse(`snow depth` == "T", "0.01", `snow depth`)),
all_precip = s_fall + rain,
has_snow = (s_fall > 0) | (s_depth > 0),
has_rain = rain > 0,
max_temp = `maximum temerature`,
min_temp = `minimum temperature`)
weather = as.data.table(weather)
weather[, c("precipitation", "snow fall", "snow depth", "maximum temerature", "minimum temperature") := NULL]
setkey(dtrain, dropoff_datetime)
setkey(weather, date)
dtrain = weather[dtrain]
날씨가 두 가지 방법으로 여행 시간에 영향을 미친다고 생각할 것입니다.
비 : 눈이 일정량의 강수량 후에 만 역할을해야합니다.
plot1 = dtrain %>%
group_by(pickup_hour, has_snow) %>%
summarise(duration = mean(trip_duration)) %>%
ggplot(aes(pickup_hour,duration, color = has_snow)) +
geom_jitter(width = 0.01, size = 2) +
labs(x = "hour", y = "trip duration")
plot2 = dtrain %>%
group_by(pickup_hour, has_snow) %>%
summarise(distance = mean(distance_km)) %>%
ggplot(aes(pickup_hour,distance, color = has_snow)) +
geom_jitter(width = 0.01, size = 2) +
labs(x = "hour", y = "Distance Covered")
grid.arrange(plot1, plot2,ncol=1)
첫 번째 줄거리에서 눈이 내릴 때 아침 10시에서 저녁 20시 사이에 눈이 내리지 않는 날에 비해 평균 여행 소요 시간이 짧다는 결론을 얻을 수 있습니다.
일반적으로 걷거나 자전거를 타는 사람들에게 눈이 내릴 때 목적지에 도달하기 위해 택시를 타기 때문에 아주 이상하지 않습니다.
plot1 = dtrain %>%
group_by(pickup_hour, has_rain) %>%
summarise(duration = mean(trip_duration)) %>%
ggplot(aes(pickup_hour,duration, color = has_rain)) +
geom_jitter(width = 0.01, size = 2) +
labs(x = "hour", y = "trip duration")
plot2 = dtrain %>%
group_by(pickup_hour, has_rain) %>%
summarise(distance = mean(distance_km)) %>%
ggplot(aes(pickup_hour,distance, color = has_rain)) +
geom_jitter(width = 0.01, size = 2) +
labs(x = "hour", y = "Distance Covered")
grid.arrange(plot1, plot2,ncol=1)
비가 내리는 날과 평균 여행 기간이 아닌 날 사이에는 별 차이가 없습니다.
그러나 나는 비오는 날에 덮힌 거리가 비오는 날과 비교하여 적다는 것을 알 수 있습니다. 일반적으로 짧은 거리를 걷거나 자전거를 타는 사람들이 목적지로 택시를 타는 것을 선호하기 때문에 이것은 논리적입니다.
2016 년 NYC의 날씨와 함께 데이터 세트를 사용하기로 결정 했으므로 이 데이터 세트를 합치려면 몇 가지 데이터가 필요합니다.
train <- fread("./data/train.csv")
Read 30.2% of 1458644 rows
Read 48.0% of 1458644 rows
Read 65.8% of 1458644 rows
Read 83.6% of 1458644 rows
Read 1458644 rows and 11 (of 11) columns from 0.187 GB file in 00:00:06
weather_nyc <- fread("./data/KNYC_Metars.csv")
train[, pi_dt_shift := paste(substr(pickup_datetime, 1, 13), ":00:00", sep = "")]
train[, df_dt_shift := paste(substr(dropoff_datetime, 1, 13), ":00:00", sep = "")]
train_joined <- dplyr::left_join(train, weather_nyc, by = c("pi_dt_shift" = "Time"))
train_joined$Conditions[is.na(train_joined$Conditions) == TRUE] <- "Unknown"
weather_condition_freq <- train_joined %>%
group_by(Conditions) %>%
select(Conditions,trip_duration ) %>%
summarize(count = n(),
mean_dur = mean(trip_duration, na.rm = TRUE),
sd_dur = sd(trip_duration, na.rm = TRUE),
median_dur = median(trip_duration, na.rm = TRUE))
datatable(weather_condition_freq)
아래의 그림은 사용자가 픽업 택시를 다른 기상 조건에 얼마나 자주 의존하는지 보여줍니다.
NA가 있는 조건 값을 ’알수 없는 카테고리’로 변경하기로 결정했습니다.
가장 빈번한 그룹은 ‘Clear’ 조건을 가진 그룹이라는 것이 분명합니다.
highchart()%>%
hc_add_series(weather_condition_freq, "spline", hcaes(x = Conditions, y = mean_dur), name = "Mean Trip Duration") %>%
hc_add_series(weather_condition_freq, "spline", hcaes(x = Conditions, y = median_dur), name = "Median Trip Duration") %>%
hc_add_series(weather_condition_freq, "spline", hcaes(x = Conditions, y = sd_dur), name = "SD Trip Duration") %>%
hc_plotOptions(series = list(
showInLegend = TRUE,
pointFormat = "{point.y}%"
),
column = list(colorByPoint = TRUE)) %>%
hc_subtitle(text = "Count by Conditions Caegories") %>%
hc_credits(
enabled = TRUE,
text = "Source: Kaggle",
href = "https://kaggle.com/damianpanek",
style = list(fontSize = "12px")
) %>%
hc_add_theme(hc_theme_google())
train_joined <- data.table(train_joined)
train_joined <- train_joined[is.na(pickup_datetime) == FALSE, ]
train_joined[, pickup_datetime := as.POSIXct(pickup_datetime, format = "%Y-%m-%d %H:%M:%S")]
train_joined[, dropoff_datetime := as.POSIXct(dropoff_datetime, format = "%Y-%m-%d %H:%M:%S")]
train_joined[, pickup_day := format(pickup_datetime, "%Y-%m-%d")]
train_joined[, pickup_month := format(pickup_datetime, "%Y-%m")]
train_joined[, dropoff_day := format(dropoff_datetime, "%Y-%m-%d")]
train_joined[, dropoff_month := format(dropoff_datetime, "%Y-%m")]
train_joined[, weekday := weekdays(pickup_datetime)]
weather_temp_day <- train_joined %>%
group_by(pickup_day) %>%
select(pickup_day, Temp., Conditions) %>%
summarize(count = n(),
min = min(Temp., na.rm = TRUE),
max = max(Temp., na.rm = TRUE),
sd_dur = sd(Temp., na.rm = TRUE))
hchart(weather_temp_day,
type = "columnrange",
hcaes(x = pickup_day, low = min, high = max, color = sd_dur)) %>%
hc_chart(polar = TRUE) %>%
hc_yAxis(max = 30, min = -10, labels = list(format = "{value} "),
showFirstLabel = FALSE) %>%
hc_xAxis(
title = list(text = ""), gridLineWidth = 0.5,
labels = list(format = "{value: %b}")) %>%
hc_add_theme(hc_theme_google()) %>%
hc_title(text = "Min/Max temperature daily, coloured by SD(Temp)")
#install.packages('leaflet.extras')
library(leaflet)
library(leaflet.extras)
lon_lat <- train_joined[, c("pickup_longitude", "pickup_latitude",
"dropoff_longitude", "dropoff_latitude")]
lon_lat$rown <- as.numeric(rownames(lon_lat))
lon_min <- lon_lat[rown < 300 ,]
str(lon_min)
Classes ‘data.table’ and 'data.frame': 299 obs. of 5 variables:
$ pickup_longitude : num -74 -74 -74 -74 -74 ...
$ pickup_latitude : num 40.8 40.7 40.8 40.7 40.8 ...
$ dropoff_longitude: num -74 -74 -74 -74 -74 ...
$ dropoff_latitude : num 40.8 40.7 40.7 40.7 40.8 ...
$ rown : num 1 2 3 4 5 6 7 8 9 10 ...
- attr(*, ".internal.selfref")=<externalptr>
drop <- lon_min[, c("pickup_longitude", "pickup_latitude", "rown")]
pick <- lon_min[, c("dropoff_longitude", "dropoff_latitude", "rown")]
colnames(drop) <- c("lon", "lat", "rown")
colnames(pick) <- colnames(drop)
all_bin_min <- bind_rows(drop, pick)
all_bin_min$rown2 <- rep(1:nrow(all_bin_min)+1/2,each = 2)
Supplied 1196 items to be assigned to 598 items of column 'rown2' (598 unused)
leaflet(data = all_bin_min) %>% addTiles() %>%
addCircles(~lon, ~lat) %>%
addPolygons(data = all_bin_min, lng = ~lon,
lat = ~lat,
stroke = 0.03, color = "blue", weight = 0.4,
opacity = 1.2) %>% enableMeasurePath()
leaflet(data = train_joined[1:50000, ]) %>% addTiles() %>%
addMarkers(~pickup_longitude, ~pickup_latitude, clusterOptions = markerClusterOptions())
train_count <- train_joined %>%
select(pickup_latitude, pickup_longitude) %>%
group_by(pickup_latitude, pickup_longitude) %>%
summarize(count = n())
train_count <- train_count[train_count$count >1,]
leaflet(data = train_count) %>% addTiles() %>%
addHeatmap(lng = ~pickup_longitude, lat = ~pickup_latitude, intensity = ~count,
blur = 20, max = 0.05, radius = 15)
train_count <- train_joined %>%
select(pickup_latitude, pickup_longitude, pickup_month) %>%
group_by(pickup_latitude, pickup_longitude, pickup_month) %>%
summarize(count = n())
train_count <- train_count[train_count$count >1,]
leaflet(data = train_count) %>% addTiles() %>%
addHeatmap(lng = ~pickup_longitude, lat = ~pickup_latitude,
layerId = ~pickup_month, group = ~pickup_month, intensity = ~count,
blur = 20, max = 0.05, radius = 15)
count_weekday <- train_joined %>%
select(weekday) %>%
group_by(weekday) %>%
summarize(count = n())
count_weekday <- data.table(count_weekday)
count_weekday <- count_weekday[is.na(weekday) == FALSE, ]
count_weekday <- data.frame(count_weekday)
tm <- treemap(count_weekday , index = c("weekday"),
vSize = "count")
hctreemap(tm)